Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
data_dir = '/input'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Downloading mnist: 9.92MB [00:02, 4.91MB/s]                            
Extracting mnist: 100%|██████████| 60.0K/60.0K [00:11<00:00, 5.25KFile/s]
Downloading celeba: 1.44GB [00:24, 60.0MB/s]                               
Extracting celeba...

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x7f4f10297a20>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x7f4f122e64a8>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.1.0
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    input_real = tf.placeholder(tf.float32, shape=[None, image_width, image_height, image_channels], name='input_real') 
    input_z = tf.placeholder(tf.float32, shape=[None, z_dim], name='input_z')
    learning_rate = tf.placeholder(tf.float32, shape=None, name='learning_rate')
    return input_real, input_z, learning_rate



"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [6]:
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    alpha=0.2
    x = images
    with tf.variable_scope('discriminator', reuse=reuse):
        x1 = tf.layers.conv2d(x, 64, 5, strides=5, padding="same")
        bn1 = tf.layers.batch_normalization(x1, training=True)
        relu1 = tf.maximum(alpha * bn1, bn1)
        
        x2 = tf.layers.conv2d(relu1, 128, 5, strides=2, padding="same")
        bn2 = tf.layers.batch_normalization(x2, training=True)
        relu2 = tf.maximum(alpha * bn2, bn2)
        
        x3 = tf.layers.conv2d(relu2, 256, 5, strides=2, padding="same")
        bn3 = tf.layers.batch_normalization(x3, training=True)
        relu3 = tf.maximum(alpha * bn3, bn3)
        
        flat = tf.reshape(relu3, (-1, 4 * 4 * 256))
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)

    return out, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [7]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    # TODO: Implement Function
    alpha=0.2
    reuse = not is_train
    with tf.variable_scope('generator', reuse=reuse):
        x1 = tf.layers.dense(z, 4 * 4 * 512)     
        x1 = tf.reshape(x1, (-1, 4, 4, 512))
        x1 = tf.layers.batch_normalization(x1, training=is_train)
        x1 = tf.maximum(alpha * x1, x1)
        
        x2 = tf.layers.conv2d_transpose(x1, 256, 5, strides=2, padding="same")
        x2 = tf.layers.batch_normalization(x2,training=is_train)
        x2 = tf.maximum(alpha * x2, x2)
        
        x3 = tf.layers.conv2d_transpose(x2, 128, 5, strides=2, padding="same")
        x3 = tf.layers.batch_normalization(x3,training=is_train)
        x3 = tf.maximum(alpha * x3, x3)
        
        logits = tf.layers.conv2d_transpose(x3, out_channel_dim, 5, strides=2, padding="same")
        out = tf.tanh(logits)
    return out

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [8]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    d_model_real, d_logits_real = discriminator(input_real, reuse=False)
    fake = generator(input_z, out_channel_dim, is_train=True)
    d_logits_fake = discriminator(fake, reuse=True)
    d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real,labels=tf.ones_like(d_logits_real) * (0.9)))
    d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake,labels=tf.zeros_like(d_logits_fake)))
    d_loss = d_loss_real + d_loss_fake
    g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_logits_fake)))
    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [9]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
    t_vars = tf.trainable_variables()
    g_vars = [var for var in t_vars if var.name.startswith('generator')]
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    all_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
    g_update_ops = [var for var in all_update_ops if var.name.startswith('generator')]
    d_update_ops = [var for var in all_update_ops if var.name.startswith('discriminator')]
    
    with tf.control_dependencies(d_update_ops):
        d_train_opt = tf.train.AdamOptimizer(learning_rate,beta1=beta1).minimize(d_loss, var_list=d_vars)
    with tf.control_dependencies(g_update_ops):
        g_train_opt = tf.train.AdamOptimizer(learning_rate,beta1=beta1).minimize(g_loss, var_list=g_vars)
    return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [10]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [11]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # TODO: Build Model
    inputs_real, inputs_z, lr = model_inputs(data_shape[1], data_shape[2], data_shape[3], z_dim)
    d_loss, g_loss = model_loss(inputs_real, inputs_z, data_shape[-1])
    d_train_opt, g_train_opt = model_opt(d_loss, g_loss, learning_rate, beta1)
    step = 0 
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                # TODO: Train Model
                step += 1
                batch_images *= 2
                # Sample random noise for G
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                #print('batch_z shape=',batch_z.shape)
                # Run optimizers
                _ = sess.run(d_train_opt, feed_dict={inputs_real: batch_images, inputs_z: batch_z, lr:learning_rate})
                _ = sess.run(g_train_opt, feed_dict={inputs_z: batch_z, lr:learning_rate})
                
                if step % 100 == 0:
                    train_loss_d = d_loss.eval({inputs_z:batch_z, inputs_real: batch_images})
                    train_loss_g = g_loss.eval({inputs_z:batch_z})
                    print("Epoch {}/{} Step {}...".format(epoch_i+1, epoch_count, step),
                      "Discriminator Loss: {:.4f}...".format(train_loss_d),
                      "Generator Loss: {:.4f}".format(train_loss_g))    

                if step % 200 == 0:
                    show_generator_output(sess, 25, inputs_z, data_shape[3], data_image_mode)

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [12]:
batch_size = 64
z_dim = 100
learning_rate = 0.0001
beta1 = 0.2


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 10

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/10 Step 100... Discriminator Loss: 1.2081... Generator Loss: 1.3460
Epoch 1/10 Step 200... Discriminator Loss: 1.1805... Generator Loss: 1.0290
Epoch 1/10 Step 300... Discriminator Loss: 1.3500... Generator Loss: 0.4858
Epoch 1/10 Step 400... Discriminator Loss: 1.0967... Generator Loss: 0.7441
Epoch 1/10 Step 500... Discriminator Loss: 1.1290... Generator Loss: 0.6785
Epoch 1/10 Step 600... Discriminator Loss: 1.3110... Generator Loss: 1.1425
Epoch 1/10 Step 700... Discriminator Loss: 1.1678... Generator Loss: 0.6432
Epoch 1/10 Step 800... Discriminator Loss: 1.1521... Generator Loss: 0.7883
Epoch 1/10 Step 900... Discriminator Loss: 1.4626... Generator Loss: 0.4147
Epoch 2/10 Step 1000... Discriminator Loss: 1.2927... Generator Loss: 1.0636
Epoch 2/10 Step 1100... Discriminator Loss: 1.4098... Generator Loss: 0.4361
Epoch 2/10 Step 1200... Discriminator Loss: 1.4671... Generator Loss: 0.4150
Epoch 2/10 Step 1300... Discriminator Loss: 1.5276... Generator Loss: 0.3887
Epoch 2/10 Step 1400... Discriminator Loss: 1.3051... Generator Loss: 0.5026
Epoch 2/10 Step 1500... Discriminator Loss: 1.2988... Generator Loss: 1.1336
Epoch 2/10 Step 1600... Discriminator Loss: 1.2410... Generator Loss: 0.7966
Epoch 2/10 Step 1700... Discriminator Loss: 1.1725... Generator Loss: 0.6271
Epoch 2/10 Step 1800... Discriminator Loss: 1.0961... Generator Loss: 0.7787
Epoch 3/10 Step 1900... Discriminator Loss: 1.2016... Generator Loss: 1.2016
Epoch 3/10 Step 2000... Discriminator Loss: 1.1497... Generator Loss: 0.6671
Epoch 3/10 Step 2100... Discriminator Loss: 1.1161... Generator Loss: 0.7018
Epoch 3/10 Step 2200... Discriminator Loss: 1.1800... Generator Loss: 1.4004
Epoch 3/10 Step 2300... Discriminator Loss: 0.9522... Generator Loss: 1.2531
Epoch 3/10 Step 2400... Discriminator Loss: 0.9568... Generator Loss: 1.0527
Epoch 3/10 Step 2500... Discriminator Loss: 1.6930... Generator Loss: 0.3526
Epoch 3/10 Step 2600... Discriminator Loss: 1.2292... Generator Loss: 1.2717
Epoch 3/10 Step 2700... Discriminator Loss: 2.0299... Generator Loss: 0.2927
Epoch 3/10 Step 2800... Discriminator Loss: 1.0036... Generator Loss: 1.0434
Epoch 4/10 Step 2900... Discriminator Loss: 1.1071... Generator Loss: 0.6962
Epoch 4/10 Step 3000... Discriminator Loss: 0.9343... Generator Loss: 1.0036
Epoch 4/10 Step 3100... Discriminator Loss: 1.4666... Generator Loss: 0.4439
Epoch 4/10 Step 3200... Discriminator Loss: 0.9468... Generator Loss: 1.0126
Epoch 4/10 Step 3300... Discriminator Loss: 0.9165... Generator Loss: 1.0521
Epoch 4/10 Step 3400... Discriminator Loss: 1.0102... Generator Loss: 0.8430
Epoch 4/10 Step 3500... Discriminator Loss: 0.9474... Generator Loss: 0.9797
Epoch 4/10 Step 3600... Discriminator Loss: 1.0327... Generator Loss: 0.7988
Epoch 4/10 Step 3700... Discriminator Loss: 2.0810... Generator Loss: 0.2829
Epoch 5/10 Step 3800... Discriminator Loss: 1.0289... Generator Loss: 0.8191
Epoch 5/10 Step 3900... Discriminator Loss: 1.0279... Generator Loss: 1.3765
Epoch 5/10 Step 4000... Discriminator Loss: 0.9018... Generator Loss: 1.2091
Epoch 5/10 Step 4100... Discriminator Loss: 0.9251... Generator Loss: 1.1714
Epoch 5/10 Step 4200... Discriminator Loss: 1.0526... Generator Loss: 0.7617
Epoch 5/10 Step 4300... Discriminator Loss: 1.1384... Generator Loss: 0.6694
Epoch 5/10 Step 4400... Discriminator Loss: 1.0671... Generator Loss: 0.7910
Epoch 5/10 Step 4500... Discriminator Loss: 1.3081... Generator Loss: 0.5558
Epoch 5/10 Step 4600... Discriminator Loss: 1.1542... Generator Loss: 0.6625
Epoch 6/10 Step 4700... Discriminator Loss: 0.8744... Generator Loss: 1.2796
Epoch 6/10 Step 4800... Discriminator Loss: 1.0519... Generator Loss: 1.2609
Epoch 6/10 Step 4900... Discriminator Loss: 0.9296... Generator Loss: 0.9967
Epoch 6/10 Step 5000... Discriminator Loss: 1.0220... Generator Loss: 0.8506
Epoch 6/10 Step 5100... Discriminator Loss: 0.9834... Generator Loss: 0.9035
Epoch 6/10 Step 5200... Discriminator Loss: 0.9002... Generator Loss: 1.2157
Epoch 6/10 Step 5300... Discriminator Loss: 0.8586... Generator Loss: 1.2117
Epoch 6/10 Step 5400... Discriminator Loss: 0.9763... Generator Loss: 0.9407
Epoch 6/10 Step 5500... Discriminator Loss: 0.9370... Generator Loss: 1.1868
Epoch 6/10 Step 5600... Discriminator Loss: 1.2528... Generator Loss: 0.5854
Epoch 7/10 Step 5700... Discriminator Loss: 1.0569... Generator Loss: 0.8221
Epoch 7/10 Step 5800... Discriminator Loss: 0.9332... Generator Loss: 1.3633
Epoch 7/10 Step 5900... Discriminator Loss: 0.9005... Generator Loss: 1.0914
Epoch 7/10 Step 6000... Discriminator Loss: 1.3284... Generator Loss: 0.5356
Epoch 7/10 Step 6100... Discriminator Loss: 1.0771... Generator Loss: 0.7392
Epoch 7/10 Step 6200... Discriminator Loss: 0.9291... Generator Loss: 1.0673
Epoch 7/10 Step 6300... Discriminator Loss: 1.8814... Generator Loss: 0.3466
Epoch 7/10 Step 6400... Discriminator Loss: 1.8681... Generator Loss: 0.3210
Epoch 7/10 Step 6500... Discriminator Loss: 1.0112... Generator Loss: 1.3491
Epoch 8/10 Step 6600... Discriminator Loss: 0.9687... Generator Loss: 0.9505
Epoch 8/10 Step 6700... Discriminator Loss: 1.1540... Generator Loss: 0.6540
Epoch 8/10 Step 6800... Discriminator Loss: 1.3083... Generator Loss: 0.5235
Epoch 8/10 Step 6900... Discriminator Loss: 0.9606... Generator Loss: 1.1030
Epoch 8/10 Step 7000... Discriminator Loss: 0.8581... Generator Loss: 1.1867
Epoch 8/10 Step 7100... Discriminator Loss: 0.8108... Generator Loss: 1.4015
Epoch 8/10 Step 7200... Discriminator Loss: 0.9578... Generator Loss: 0.9928
Epoch 8/10 Step 7300... Discriminator Loss: 0.9919... Generator Loss: 0.8944
Epoch 8/10 Step 7400... Discriminator Loss: 0.9721... Generator Loss: 0.9047
Epoch 9/10 Step 7500... Discriminator Loss: 1.1537... Generator Loss: 0.6628
Epoch 9/10 Step 7600... Discriminator Loss: 0.9416... Generator Loss: 0.9979
Epoch 9/10 Step 7700... Discriminator Loss: 0.8952... Generator Loss: 1.1046
Epoch 9/10 Step 7800... Discriminator Loss: 1.1987... Generator Loss: 0.6904
Epoch 9/10 Step 7900... Discriminator Loss: 0.8463... Generator Loss: 1.2610
Epoch 9/10 Step 8000... Discriminator Loss: 1.2481... Generator Loss: 0.6140
Epoch 9/10 Step 8100... Discriminator Loss: 0.9754... Generator Loss: 0.8739
Epoch 9/10 Step 8200... Discriminator Loss: 0.8228... Generator Loss: 1.2809
Epoch 9/10 Step 8300... Discriminator Loss: 1.0061... Generator Loss: 1.3690
Epoch 9/10 Step 8400... Discriminator Loss: 1.1136... Generator Loss: 0.7196
Epoch 10/10 Step 8500... Discriminator Loss: 1.0408... Generator Loss: 0.8992
Epoch 10/10 Step 8600... Discriminator Loss: 0.7502... Generator Loss: 1.7393
Epoch 10/10 Step 8700... Discriminator Loss: 1.0761... Generator Loss: 0.7866
Epoch 10/10 Step 8800... Discriminator Loss: 0.9164... Generator Loss: 1.0683
Epoch 10/10 Step 8900... Discriminator Loss: 1.3260... Generator Loss: 0.5837
Epoch 10/10 Step 9000... Discriminator Loss: 1.3439... Generator Loss: 0.5646
Epoch 10/10 Step 9100... Discriminator Loss: 0.8697... Generator Loss: 1.1303
Epoch 10/10 Step 9200... Discriminator Loss: 0.8800... Generator Loss: 1.2138
Epoch 10/10 Step 9300... Discriminator Loss: 0.9732... Generator Loss: 0.9177

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [12]:
batch_size = 16
z_dim = 100
learning_rate = 0.0001
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/2 Step 100... Discriminator Loss: 0.9044... Generator Loss: 1.0502
Epoch 1/2 Step 200... Discriminator Loss: 0.9327... Generator Loss: 1.0493
Epoch 1/2 Step 300... Discriminator Loss: 0.7904... Generator Loss: 1.7237
Epoch 1/2 Step 400... Discriminator Loss: 0.7103... Generator Loss: 2.0090
Epoch 1/2 Step 500... Discriminator Loss: 0.7506... Generator Loss: 1.9516
Epoch 1/2 Step 600... Discriminator Loss: 0.7258... Generator Loss: 2.1374
Epoch 1/2 Step 700... Discriminator Loss: 0.7140... Generator Loss: 1.9919
Epoch 1/2 Step 800... Discriminator Loss: 0.7458... Generator Loss: 2.0010
Epoch 1/2 Step 900... Discriminator Loss: 0.7449... Generator Loss: 1.8543
Epoch 1/2 Step 1000... Discriminator Loss: 0.7007... Generator Loss: 2.3724
Epoch 1/2 Step 1100... Discriminator Loss: 0.7245... Generator Loss: 2.0682
Epoch 1/2 Step 1200... Discriminator Loss: 0.7067... Generator Loss: 2.4717
Epoch 1/2 Step 1300... Discriminator Loss: 0.8617... Generator Loss: 1.3396
Epoch 1/2 Step 1400... Discriminator Loss: 0.8873... Generator Loss: 1.5587
Epoch 1/2 Step 1500... Discriminator Loss: 0.9550... Generator Loss: 1.0980
Epoch 1/2 Step 1600... Discriminator Loss: 1.0501... Generator Loss: 0.9595
Epoch 1/2 Step 1700... Discriminator Loss: 1.0602... Generator Loss: 0.9324
Epoch 1/2 Step 1800... Discriminator Loss: 1.1370... Generator Loss: 0.9715
Epoch 1/2 Step 1900... Discriminator Loss: 1.2721... Generator Loss: 0.6748
Epoch 1/2 Step 2000... Discriminator Loss: 1.1649... Generator Loss: 0.8942
Epoch 1/2 Step 2100... Discriminator Loss: 1.2768... Generator Loss: 0.6002
Epoch 1/2 Step 2200... Discriminator Loss: 1.3479... Generator Loss: 0.6238
Epoch 1/2 Step 2300... Discriminator Loss: 1.1571... Generator Loss: 0.8086
Epoch 1/2 Step 2400... Discriminator Loss: 1.0721... Generator Loss: 0.9048
Epoch 1/2 Step 2500... Discriminator Loss: 1.4171... Generator Loss: 0.4872
Epoch 1/2 Step 2600... Discriminator Loss: 1.4885... Generator Loss: 0.5292
Epoch 1/2 Step 2700... Discriminator Loss: 1.2526... Generator Loss: 0.6408
Epoch 1/2 Step 2800... Discriminator Loss: 1.3565... Generator Loss: 0.6445
Epoch 1/2 Step 2900... Discriminator Loss: 1.0991... Generator Loss: 0.8151
Epoch 1/2 Step 3000... Discriminator Loss: 1.2469... Generator Loss: 0.5922
Epoch 1/2 Step 3100... Discriminator Loss: 1.1483... Generator Loss: 0.7543
Epoch 1/2 Step 3200... Discriminator Loss: 1.1235... Generator Loss: 1.0190
Epoch 1/2 Step 3300... Discriminator Loss: 1.1744... Generator Loss: 0.6994
Epoch 1/2 Step 3400... Discriminator Loss: 1.1244... Generator Loss: 1.0644
Epoch 1/2 Step 3500... Discriminator Loss: 1.2375... Generator Loss: 0.6559
Epoch 1/2 Step 3600... Discriminator Loss: 0.9907... Generator Loss: 1.0954
Epoch 1/2 Step 3700... Discriminator Loss: 1.3109... Generator Loss: 0.4974
Epoch 1/2 Step 3800... Discriminator Loss: 1.1270... Generator Loss: 0.7568
Epoch 1/2 Step 3900... Discriminator Loss: 1.1488... Generator Loss: 0.9050
Epoch 1/2 Step 4000... Discriminator Loss: 1.1061... Generator Loss: 0.7642
Epoch 1/2 Step 4100... Discriminator Loss: 1.1245... Generator Loss: 0.8373
Epoch 1/2 Step 4200... Discriminator Loss: 1.6684... Generator Loss: 0.3305
Epoch 1/2 Step 4300... Discriminator Loss: 1.0816... Generator Loss: 0.9015
Epoch 1/2 Step 4400... Discriminator Loss: 1.2417... Generator Loss: 0.6056
Epoch 1/2 Step 4500... Discriminator Loss: 1.0800... Generator Loss: 0.8728
Epoch 1/2 Step 4600... Discriminator Loss: 0.9316... Generator Loss: 1.1047
Epoch 1/2 Step 4700... Discriminator Loss: 1.0607... Generator Loss: 0.8158
Epoch 1/2 Step 4800... Discriminator Loss: 1.5334... Generator Loss: 0.3885
Epoch 1/2 Step 4900... Discriminator Loss: 0.9752... Generator Loss: 1.0548
Epoch 1/2 Step 5000... Discriminator Loss: 1.2162... Generator Loss: 0.7153
Epoch 1/2 Step 5100... Discriminator Loss: 0.9100... Generator Loss: 1.2129
Epoch 1/2 Step 5200... Discriminator Loss: 0.7983... Generator Loss: 1.4823
Epoch 1/2 Step 5300... Discriminator Loss: 1.1877... Generator Loss: 1.7866
Epoch 1/2 Step 5400... Discriminator Loss: 1.1267... Generator Loss: 0.9007
Epoch 1/2 Step 5500... Discriminator Loss: 0.9682... Generator Loss: 0.8789
Epoch 1/2 Step 5600... Discriminator Loss: 0.9770... Generator Loss: 1.0275
Epoch 1/2 Step 5700... Discriminator Loss: 0.8650... Generator Loss: 1.3378
Epoch 1/2 Step 5800... Discriminator Loss: 0.9636... Generator Loss: 0.9993
Epoch 1/2 Step 5900... Discriminator Loss: 1.0018... Generator Loss: 0.9760
Epoch 1/2 Step 6000... Discriminator Loss: 0.8687... Generator Loss: 1.2090
Epoch 1/2 Step 6100... Discriminator Loss: 1.3140... Generator Loss: 0.5499
Epoch 1/2 Step 6200... Discriminator Loss: 1.4652... Generator Loss: 0.4241
Epoch 1/2 Step 6300... Discriminator Loss: 1.3138... Generator Loss: 0.6084
Epoch 1/2 Step 6400... Discriminator Loss: 0.8639... Generator Loss: 1.3455
Epoch 1/2 Step 6500... Discriminator Loss: 1.0927... Generator Loss: 0.7491
Epoch 1/2 Step 6600... Discriminator Loss: 1.4642... Generator Loss: 0.4261
Epoch 1/2 Step 6700... Discriminator Loss: 0.9155... Generator Loss: 1.0377
Epoch 1/2 Step 6800... Discriminator Loss: 1.1817... Generator Loss: 0.6130
Epoch 1/2 Step 6900... Discriminator Loss: 0.8643... Generator Loss: 1.2059
Epoch 1/2 Step 7000... Discriminator Loss: 1.0346... Generator Loss: 0.8491
Epoch 1/2 Step 7100... Discriminator Loss: 1.0367... Generator Loss: 0.9262
Epoch 1/2 Step 7200... Discriminator Loss: 1.1053... Generator Loss: 0.9804
Epoch 1/2 Step 7300... Discriminator Loss: 1.1680... Generator Loss: 0.8404
Epoch 1/2 Step 7400... Discriminator Loss: 0.9079... Generator Loss: 1.0327
Epoch 1/2 Step 7500... Discriminator Loss: 1.0809... Generator Loss: 0.9270
Epoch 1/2 Step 7600... Discriminator Loss: 1.0881... Generator Loss: 0.8183
Epoch 1/2 Step 7700... Discriminator Loss: 1.4947... Generator Loss: 0.4121
Epoch 1/2 Step 7800... Discriminator Loss: 1.0663... Generator Loss: 0.8386
Epoch 1/2 Step 7900... Discriminator Loss: 0.9584... Generator Loss: 1.0066
Epoch 1/2 Step 8000... Discriminator Loss: 1.0563... Generator Loss: 0.8201
Epoch 1/2 Step 8100... Discriminator Loss: 1.5165... Generator Loss: 0.9228
Epoch 1/2 Step 8200... Discriminator Loss: 1.1745... Generator Loss: 0.6175
Epoch 1/2 Step 8300... Discriminator Loss: 1.1131... Generator Loss: 0.8638
Epoch 1/2 Step 8400... Discriminator Loss: 0.9337... Generator Loss: 0.9356
Epoch 1/2 Step 8500... Discriminator Loss: 1.3021... Generator Loss: 0.5707
Epoch 1/2 Step 8600... Discriminator Loss: 0.8533... Generator Loss: 1.2092
Epoch 1/2 Step 8700... Discriminator Loss: 0.8099... Generator Loss: 1.3901
Epoch 1/2 Step 8800... Discriminator Loss: 1.3193... Generator Loss: 0.5417
Epoch 1/2 Step 8900... Discriminator Loss: 1.0302... Generator Loss: 0.8312
Epoch 1/2 Step 9000... Discriminator Loss: 1.1538... Generator Loss: 0.6613
Epoch 1/2 Step 9100... Discriminator Loss: 1.6629... Generator Loss: 0.3526
Epoch 1/2 Step 9200... Discriminator Loss: 1.0143... Generator Loss: 0.9515
Epoch 1/2 Step 9300... Discriminator Loss: 0.8051... Generator Loss: 1.4269
Epoch 1/2 Step 9400... Discriminator Loss: 1.0100... Generator Loss: 0.8568
Epoch 1/2 Step 9500... Discriminator Loss: 1.1794... Generator Loss: 0.6548
Epoch 1/2 Step 9600... Discriminator Loss: 0.8535... Generator Loss: 1.2129
Epoch 1/2 Step 9700... Discriminator Loss: 0.9913... Generator Loss: 0.9426
Epoch 1/2 Step 9800... Discriminator Loss: 1.0355... Generator Loss: 1.0257
Epoch 1/2 Step 9900... Discriminator Loss: 0.9486... Generator Loss: 0.9281
Epoch 1/2 Step 10000... Discriminator Loss: 1.0715... Generator Loss: 0.7811
Epoch 1/2 Step 10100... Discriminator Loss: 1.1330... Generator Loss: 0.7004
Epoch 1/2 Step 10200... Discriminator Loss: 0.9346... Generator Loss: 1.1907
Epoch 1/2 Step 10300... Discriminator Loss: 1.0831... Generator Loss: 0.8169
Epoch 1/2 Step 10400... Discriminator Loss: 1.3261... Generator Loss: 0.4978
Epoch 1/2 Step 10500... Discriminator Loss: 0.8614... Generator Loss: 1.4341
Epoch 1/2 Step 10600... Discriminator Loss: 0.9397... Generator Loss: 0.9305
Epoch 1/2 Step 10700... Discriminator Loss: 1.0204... Generator Loss: 0.8895
Epoch 1/2 Step 10800... Discriminator Loss: 0.9929... Generator Loss: 0.9444
Epoch 1/2 Step 10900... Discriminator Loss: 0.8468... Generator Loss: 1.4417
Epoch 1/2 Step 11000... Discriminator Loss: 0.8560... Generator Loss: 1.2341
Epoch 1/2 Step 11100... Discriminator Loss: 0.8583... Generator Loss: 1.5639
Epoch 1/2 Step 11200... Discriminator Loss: 0.7504... Generator Loss: 1.8797
Epoch 1/2 Step 11300... Discriminator Loss: 1.3978... Generator Loss: 0.5212
Epoch 1/2 Step 11400... Discriminator Loss: 0.8783... Generator Loss: 1.2189
Epoch 1/2 Step 11500... Discriminator Loss: 0.8623... Generator Loss: 1.2768
Epoch 1/2 Step 11600... Discriminator Loss: 1.0568... Generator Loss: 0.7592
Epoch 1/2 Step 11700... Discriminator Loss: 1.0807... Generator Loss: 0.8932
Epoch 1/2 Step 11800... Discriminator Loss: 0.8480... Generator Loss: 1.4000
Epoch 1/2 Step 11900... Discriminator Loss: 0.8662... Generator Loss: 1.2420
Epoch 1/2 Step 12000... Discriminator Loss: 1.1442... Generator Loss: 0.6612
Epoch 1/2 Step 12100... Discriminator Loss: 1.0670... Generator Loss: 0.7465
Epoch 1/2 Step 12200... Discriminator Loss: 1.2983... Generator Loss: 0.6204
Epoch 1/2 Step 12300... Discriminator Loss: 0.9556... Generator Loss: 0.9649
Epoch 1/2 Step 12400... Discriminator Loss: 1.0461... Generator Loss: 0.8396
Epoch 1/2 Step 12500... Discriminator Loss: 1.0342... Generator Loss: 0.8373
Epoch 1/2 Step 12600... Discriminator Loss: 1.0038... Generator Loss: 0.8376
Epoch 2/2 Step 12700... Discriminator Loss: 1.4653... Generator Loss: 0.4377
Epoch 2/2 Step 12800... Discriminator Loss: 1.1212... Generator Loss: 0.7266
Epoch 2/2 Step 12900... Discriminator Loss: 1.5295... Generator Loss: 0.4290
Epoch 2/2 Step 13000... Discriminator Loss: 0.9190... Generator Loss: 0.9971
Epoch 2/2 Step 13100... Discriminator Loss: 0.8025... Generator Loss: 1.6928
Epoch 2/2 Step 13200... Discriminator Loss: 0.7436... Generator Loss: 1.8973
Epoch 2/2 Step 13300... Discriminator Loss: 1.1543... Generator Loss: 0.6781
Epoch 2/2 Step 13400... Discriminator Loss: 1.0018... Generator Loss: 0.8682
Epoch 2/2 Step 13500... Discriminator Loss: 1.2402... Generator Loss: 0.5619
Epoch 2/2 Step 13600... Discriminator Loss: 1.1899... Generator Loss: 0.6150
Epoch 2/2 Step 13700... Discriminator Loss: 0.9362... Generator Loss: 0.9938
Epoch 2/2 Step 13800... Discriminator Loss: 0.7723... Generator Loss: 1.5406
Epoch 2/2 Step 13900... Discriminator Loss: 0.9109... Generator Loss: 1.0568
Epoch 2/2 Step 14000... Discriminator Loss: 0.7881... Generator Loss: 1.4971
Epoch 2/2 Step 14100... Discriminator Loss: 0.9976... Generator Loss: 0.9034
Epoch 2/2 Step 14200... Discriminator Loss: 1.0488... Generator Loss: 0.7925
Epoch 2/2 Step 14300... Discriminator Loss: 0.9466... Generator Loss: 1.1171
Epoch 2/2 Step 14400... Discriminator Loss: 0.8640... Generator Loss: 1.1865
Epoch 2/2 Step 14500... Discriminator Loss: 0.9510... Generator Loss: 0.9536
Epoch 2/2 Step 14600... Discriminator Loss: 0.8306... Generator Loss: 1.6113
Epoch 2/2 Step 14700... Discriminator Loss: 0.7961... Generator Loss: 1.6243
Epoch 2/2 Step 14800... Discriminator Loss: 0.9079... Generator Loss: 1.2078
Epoch 2/2 Step 14900... Discriminator Loss: 0.7828... Generator Loss: 1.5434
Epoch 2/2 Step 15000... Discriminator Loss: 1.1147... Generator Loss: 0.7925
Epoch 2/2 Step 15100... Discriminator Loss: 0.8776... Generator Loss: 1.3862
Epoch 2/2 Step 15200... Discriminator Loss: 0.7088... Generator Loss: 2.5191
Epoch 2/2 Step 15300... Discriminator Loss: 0.8627... Generator Loss: 1.0971
Epoch 2/2 Step 15400... Discriminator Loss: 0.9069... Generator Loss: 1.1275
Epoch 2/2 Step 15500... Discriminator Loss: 0.9224... Generator Loss: 1.1224
Epoch 2/2 Step 15600... Discriminator Loss: 1.2622... Generator Loss: 0.5397
Epoch 2/2 Step 15700... Discriminator Loss: 0.7663... Generator Loss: 1.7587
Epoch 2/2 Step 15800... Discriminator Loss: 1.0100... Generator Loss: 0.9337
Epoch 2/2 Step 15900... Discriminator Loss: 0.8773... Generator Loss: 1.1203
Epoch 2/2 Step 16000... Discriminator Loss: 1.0781... Generator Loss: 0.7429
Epoch 2/2 Step 16100... Discriminator Loss: 0.8940... Generator Loss: 1.0543
Epoch 2/2 Step 16200... Discriminator Loss: 0.9551... Generator Loss: 0.9827
Epoch 2/2 Step 16300... Discriminator Loss: 0.9374... Generator Loss: 0.9580
Epoch 2/2 Step 16400... Discriminator Loss: 0.9960... Generator Loss: 0.8658
Epoch 2/2 Step 16500... Discriminator Loss: 0.8845... Generator Loss: 1.1593
Epoch 2/2 Step 16600... Discriminator Loss: 0.9016... Generator Loss: 1.2278
Epoch 2/2 Step 16700... Discriminator Loss: 0.9579... Generator Loss: 0.9444
Epoch 2/2 Step 16800... Discriminator Loss: 0.7691... Generator Loss: 1.6819
Epoch 2/2 Step 16900... Discriminator Loss: 0.8948... Generator Loss: 1.0618
Epoch 2/2 Step 17000... Discriminator Loss: 0.8266... Generator Loss: 1.2812
Epoch 2/2 Step 17100... Discriminator Loss: 0.9323... Generator Loss: 1.0329
Epoch 2/2 Step 17200... Discriminator Loss: 0.8028... Generator Loss: 1.3983
Epoch 2/2 Step 17300... Discriminator Loss: 1.2205... Generator Loss: 0.6270
Epoch 2/2 Step 17400... Discriminator Loss: 0.8375... Generator Loss: 1.2706
Epoch 2/2 Step 17500... Discriminator Loss: 1.1699... Generator Loss: 0.6182
Epoch 2/2 Step 17600... Discriminator Loss: 1.2133... Generator Loss: 0.6187
Epoch 2/2 Step 17700... Discriminator Loss: 0.8996... Generator Loss: 1.2296
Epoch 2/2 Step 17800... Discriminator Loss: 0.8454... Generator Loss: 1.1781
Epoch 2/2 Step 17900... Discriminator Loss: 1.0534... Generator Loss: 0.7833
Epoch 2/2 Step 18000... Discriminator Loss: 0.7657... Generator Loss: 1.9666
Epoch 2/2 Step 18100... Discriminator Loss: 0.8460... Generator Loss: 1.2578
Epoch 2/2 Step 18200... Discriminator Loss: 1.2639... Generator Loss: 0.5611
Epoch 2/2 Step 18300... Discriminator Loss: 0.7629... Generator Loss: 1.6747
Epoch 2/2 Step 18400... Discriminator Loss: 1.6309... Generator Loss: 0.4330
Epoch 2/2 Step 18500... Discriminator Loss: 0.8211... Generator Loss: 1.4546
Epoch 2/2 Step 18600... Discriminator Loss: 0.7007... Generator Loss: 2.4370
Epoch 2/2 Step 18700... Discriminator Loss: 0.8840... Generator Loss: 1.1161
Epoch 2/2 Step 18800... Discriminator Loss: 0.7099... Generator Loss: 3.1437
Epoch 2/2 Step 18900... Discriminator Loss: 0.8358... Generator Loss: 1.3667
Epoch 2/2 Step 19000... Discriminator Loss: 0.7891... Generator Loss: 1.4607
Epoch 2/2 Step 19100... Discriminator Loss: 1.1575... Generator Loss: 0.6356
Epoch 2/2 Step 19200... Discriminator Loss: 0.7197... Generator Loss: 1.9992
Epoch 2/2 Step 19300... Discriminator Loss: 0.7258... Generator Loss: 1.9473
Epoch 2/2 Step 19400... Discriminator Loss: 0.7976... Generator Loss: 1.3870
Epoch 2/2 Step 19500... Discriminator Loss: 1.0519... Generator Loss: 0.7892
Epoch 2/2 Step 19600... Discriminator Loss: 0.8568... Generator Loss: 1.2047
Epoch 2/2 Step 19700... Discriminator Loss: 0.7722... Generator Loss: 1.6051
Epoch 2/2 Step 19800... Discriminator Loss: 1.3940... Generator Loss: 0.4600
Epoch 2/2 Step 19900... Discriminator Loss: 1.0957... Generator Loss: 0.7166
Epoch 2/2 Step 20000... Discriminator Loss: 1.6204... Generator Loss: 0.3557
Epoch 2/2 Step 20100... Discriminator Loss: 0.7231... Generator Loss: 2.1775
Epoch 2/2 Step 20200... Discriminator Loss: 0.9414... Generator Loss: 0.9800
Epoch 2/2 Step 20300... Discriminator Loss: 0.9721... Generator Loss: 0.9577
Epoch 2/2 Step 20400... Discriminator Loss: 0.7179... Generator Loss: 2.0371
Epoch 2/2 Step 20500... Discriminator Loss: 1.0403... Generator Loss: 0.8099
Epoch 2/2 Step 20600... Discriminator Loss: 0.8800... Generator Loss: 1.1621
Epoch 2/2 Step 20700... Discriminator Loss: 0.7526... Generator Loss: 1.6493
Epoch 2/2 Step 20800... Discriminator Loss: 0.9269... Generator Loss: 1.0090
Epoch 2/2 Step 20900... Discriminator Loss: 1.0026... Generator Loss: 1.0842
Epoch 2/2 Step 21000... Discriminator Loss: 0.8995... Generator Loss: 1.0424
Epoch 2/2 Step 21100... Discriminator Loss: 0.7610... Generator Loss: 1.6378
Epoch 2/2 Step 21200... Discriminator Loss: 0.8027... Generator Loss: 1.3983
Epoch 2/2 Step 21300... Discriminator Loss: 0.7701... Generator Loss: 1.8636
Epoch 2/2 Step 21400... Discriminator Loss: 1.0998... Generator Loss: 0.7060
Epoch 2/2 Step 21500... Discriminator Loss: 0.8164... Generator Loss: 1.4085
Epoch 2/2 Step 21600... Discriminator Loss: 1.1053... Generator Loss: 0.7219
Epoch 2/2 Step 21700... Discriminator Loss: 0.7403... Generator Loss: 1.8561
Epoch 2/2 Step 21800... Discriminator Loss: 0.9133... Generator Loss: 1.0112
Epoch 2/2 Step 21900... Discriminator Loss: 0.7659... Generator Loss: 1.5588
Epoch 2/2 Step 22000... Discriminator Loss: 0.9387... Generator Loss: 1.0662
Epoch 2/2 Step 22100... Discriminator Loss: 1.0276... Generator Loss: 0.8773
Epoch 2/2 Step 22200... Discriminator Loss: 0.8041... Generator Loss: 1.3860
Epoch 2/2 Step 22300... Discriminator Loss: 0.9224... Generator Loss: 1.0381
Epoch 2/2 Step 22400... Discriminator Loss: 0.9481... Generator Loss: 1.0272
Epoch 2/2 Step 22500... Discriminator Loss: 0.8731... Generator Loss: 1.0926
Epoch 2/2 Step 22600... Discriminator Loss: 1.8813... Generator Loss: 0.2935
Epoch 2/2 Step 22700... Discriminator Loss: 1.0187... Generator Loss: 1.0795
Epoch 2/2 Step 22800... Discriminator Loss: 0.9127... Generator Loss: 1.0886
Epoch 2/2 Step 22900... Discriminator Loss: 0.9836... Generator Loss: 0.9684
Epoch 2/2 Step 23000... Discriminator Loss: 0.7639... Generator Loss: 1.5687
Epoch 2/2 Step 23100... Discriminator Loss: 0.7481... Generator Loss: 1.7136
Epoch 2/2 Step 23200... Discriminator Loss: 0.9499... Generator Loss: 0.9793
Epoch 2/2 Step 23300... Discriminator Loss: 0.7517... Generator Loss: 1.9268
Epoch 2/2 Step 23400... Discriminator Loss: 1.0895... Generator Loss: 0.7472
Epoch 2/2 Step 23500... Discriminator Loss: 0.9768... Generator Loss: 0.9037
Epoch 2/2 Step 23600... Discriminator Loss: 1.0313... Generator Loss: 0.8202
Epoch 2/2 Step 23700... Discriminator Loss: 0.6946... Generator Loss: 3.0390
Epoch 2/2 Step 23800... Discriminator Loss: 1.1574... Generator Loss: 0.6254
Epoch 2/2 Step 23900... Discriminator Loss: 0.7750... Generator Loss: 1.6513
Epoch 2/2 Step 24000... Discriminator Loss: 1.4851... Generator Loss: 0.4562
Epoch 2/2 Step 24100... Discriminator Loss: 0.8448... Generator Loss: 1.2583
Epoch 2/2 Step 24200... Discriminator Loss: 0.8580... Generator Loss: 1.2352
Epoch 2/2 Step 24300... Discriminator Loss: 1.0328... Generator Loss: 0.8073
Epoch 2/2 Step 24400... Discriminator Loss: 0.8414... Generator Loss: 1.2845
Epoch 2/2 Step 24500... Discriminator Loss: 0.6966... Generator Loss: 2.6832
Epoch 2/2 Step 24600... Discriminator Loss: 0.7357... Generator Loss: 1.7888
Epoch 2/2 Step 24700... Discriminator Loss: 0.7897... Generator Loss: 1.6315
Epoch 2/2 Step 24800... Discriminator Loss: 0.8192... Generator Loss: 1.3710
Epoch 2/2 Step 24900... Discriminator Loss: 0.7706... Generator Loss: 1.6221
Epoch 2/2 Step 25000... Discriminator Loss: 0.7982... Generator Loss: 1.3744
Epoch 2/2 Step 25100... Discriminator Loss: 0.8031... Generator Loss: 1.3554
Epoch 2/2 Step 25200... Discriminator Loss: 1.2496... Generator Loss: 0.5632
Epoch 2/2 Step 25300... Discriminator Loss: 1.4824... Generator Loss: 0.4328

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.